Kenneth T. Co, PhD
Assistant Professor
Academic Program Director
Master of Science in Data Science
EXPERTISE
Machine Learning, Artificial Intelligence, Cybersecurity, Computer Vision, Data Science
RESEARCH INTERESTS
Adversarial Machine Learning, Federated Learning, Anomaly Detection, Medical Imaging, Safety of Artificial Intelligence
Kenneth T. Co, PhD
Assistant Professor
Academic Program Director
Master of Science in Data Science
ACADEMIC BACKGROUND
- PhD in Computing, Imperial College London
- Master of Science in Computing (Machine Learning), Imperial College London
- Master of Arts in Mathematics, Johns Hopkins University
- Bachelor of Arts in Mathematics, Johns Hopkins University
PROFESSIONAL AND ACADEMIC EXPERIENCE
- Machine Learning Consultant, Thinking Machines
- Research Scientist, DataSpartan UK
AFFILIATIONS, AWARDS AND HONORS
- 2023 Endorsement for Global Talent Visa, Royal Academy of Engineering, United Kingdom
- 2020 3rd Place, CSAW’20 Cybersecurity Applied Research Competition, Europe, hosted by New York University (NYU) and Grenoble INP-ESISAR
- 2018-2022 PhD Programme Funding, Department of Computing, Imperial College London
- 2016 J.J. Sylvester Undergraduate Award, Department of Mathematics, Johns Hopkins University
- 2016 Applied Mathematics and Statistics Achievement Award, Department of Applied Mathematics and Statistics, Johns Hopkins University
- 2015 Naddor Prize, Department of Applied Mathematics and Statistics, Johns Hopkins University
- 2012-2016 Woodrow Wilson Research Fellow, Johns Hopkins University
- 2012 Bronze Medalist, International Mathematical Olympiad (IMO), Mar del Plata, Argentina
PEER-REVIEWED CONFERENCE PROCEEDINGS
- Castiglione, L. M., Hau, Z., Co, K. T., Muñoz-González, L., Teng, F., & Lupu, E. (2022). HA-Grid: Security Aware Hazard Analysis for Smart Grids. In Proceedings of the 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 446-452. https://doi.org/10.1109/SmartGridComm52983.2022.9961003
- Co, K. T., Muñoz-González, L., Kanthan, L., Glocker, B., & Lupu, E. C. (2021). Universal adversarial robustness of texture and shape-biased models. In Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), 799-803. https://doi.org/10.1109/ICIP42928.2021.9506325
- Co, K. T., Muñoz-González, L., de Maupeou, S., & Lupu, E. C. (2019). Procedural noise adversarial examples for black-box attacks on deep convolutional networks. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS ’19), 275–289. https://doi.org/10.1145/3319535.3345660
BOOK CHAPTER
- Muñoz-González, L., Carnerero-Cano, J., Co, K. T., & Lupu, E. C. (2019). Challenges and Advances in Adversarial Machine Learning. Resilience and Hybrid Threats, 102-120.
